Shipping LLM Features in Production (Without Rewriting Your Product)

Start with a workflow, not a platform

The teams that ship LLM features successfully pick one narrow job — classify support tickets, draft outbound copy, extract fields from PDFs — and harden that path before calling it "AI-powered."

A feasible first production use case

Good first bets share these traits:

• Human review is acceptable for the first release
• Wrong answers are annoying, not legally or financially catastrophic
• Inputs and outputs are structured enough to test automatically
• Volume is bounded so token spend is predictable

Bad first bets: fully autonomous customer-facing agents with no escalation, or "chat with all our data" without retrieval quality work.

Guardrails that actually matter

Prompt + schema, not prompt alone Ask for JSON or a fixed enum where possible. Validate before you act on the model output.

Timeouts and budgets Cap tokens per request and per tenant. Alert when daily spend doubles.

Fallbacks If the model fails, route to a human queue or a simpler rules path — never a blank screen.

Evaluation set Keep 20–50 real examples and rerun them when you change prompts or models. "Feels better" is not a release criterion.

Integration pattern that scales

Wrap the LLM behind a small service or module with: